In order to create an improved experience during the use of virtual reality (VR), an auditory virtual reality (AVR) can be created by replicating sound scattering that would occur as a sound source interacts both with a simulated representation of a physical environment and with the specific anatomy of the listener, including the listeners head, ears, and torso.
To understand a sound landscape, it is possible to measure the changes that sound undergoes as it interacts with the physical environment and the listener, as shown in the prior art, using a Head-Related Transfer Function (HRTF) that is specific to the listener. Various means for obtaining listener-specific HRTFs are shown in prior art FIGS. 1 and 2.
In FIG. 1, a source (speaker) is placed at a given location and a generated sound is then recorded using a microphone placed in the ear canal of an individual. In order to obtain the HRTF corresponding to a different source location, the speaker is moved to that location and the measurement is repeated. HRTF measurements from thousands of points are needed and the process is time-consuming, tedious, and burdensome to the listener.
In FIG. 2, a transmitter is located within the ear of the individual and a plurality of pressure wave sensors (microphones) are arranged in a microphone array surrounding the individual's head. The sound emanating from the transmitter is collected at the microphones in the form of pressure waves which are analyzed to extract the HRTF. To pinpoint the location of the sensors in reference to the transmitter, a microphone and head tracking system is attached to the individual's head to monitor position.
A Head-Related Impulse Response (HRIR) filter is a listener-dependent and direction-dependent filter which can be derived from the inverse Fourier transform of the HRTF. Knowledge of the HRIR filter is useful because it can be applied to additional sound sources which have not been measured in order to understand the reaction of these sound sources to the listener and the environment via a convolution operation.
Since the computational cost of the convolution operation depends on the size of the HRIR filter, identifying a sparse HRIR filter representation will allow efficient, zero-latency processing in a time domain as an alternative to the albeit low complexity but latency-laden processing using fast Fourier transforms (FFT) in the frequency domain.